Ship　target　detection　is　of　great　significance　in　marine　surveillance,　rescue　and　so　on.　In　this　paper,　in　order　to　improve　the　performance　of　ship　target　detection,　we　proposed　a　ship　target　detection　method　based　on　multi　task　learning.　There　are　mainly　two　contributions.　Firstly,　we　designed　a　multi-task　learning　model　by　integrating　segmentation　module　to　the　faster　RCNN　model.　Through　the　strategies　of　feature　sharing　and　joint　learning,　it　is　helpful　to　improve　the　accuracy　of　target　detection　with　the　assistance　of　segmentation;　Secondly,　in　order　to　deal　with　the　impact　of　initial　anchor　frame　scale　on　target　detection　accuracy,　we　introduced　an　adaptive　anchor　width　height　ratio　setting　method　based　on　improved　K-means　algorithm,　by　adaptively　select　initial　anchor　size　suitable　for　the　characteristics　of　ship　targets,　it　is　beneficial　to　further　improve　the　detection　accuracy.　Moreover,　we　constructed　an　extended　version　of　ship　image　data　set　including　14614　images　belonging　to　13　categories.　Experimental　results　demonstrated　that　the　proposed　model　can　effectively　improve　the　accuracy　of　ship　target　detection;　and　the　comparison　and　the　ablation　experiments　further　validated　the　strategies　of　multi-task　joint　learning　and　adaptive　anchor　size　setting　is　helpful　for　improving　the　performance　of　ship　target　detection.　©　2022　SPIE.